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Activity Number:
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399
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Type:
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Invited
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Date/Time:
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Wednesday, August 5, 2009 : 8:30 AM to 10:20 AM
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Sponsor:
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General Methodology
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| Abstract - #303598 |
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Title:
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Dimension Augmenting Vector Machine: A New General Classifier with Flexible Feature Selection in High Dimension
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Author(s):
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Samiran Ghosh*+
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Companies:
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Indiana University Purdue University Indianapolis
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Address:
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402 N. Blackford Street, LD270, Indianapolis, IN, 46202-3216 ,
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Keywords:
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Classification ; Import Vector Machine ; Radial Basis Function ; Regularization ; Support Vector Machine ; Variable Selection
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Abstract:
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Support vector machine and other reproducing kernel Hilbert space based classifier systems are drawing much attention recently due to its robustness and generalization capability. All of these approaches construct classifiers based on training sample in a high dimensional space by using all available dimensions. SVM achieves huge data compression by selecting only few observations which are lying in the boundary of the classifier function. However when the number of observations are not very large (small n) but the number of dimensions are very large (large p), then it is not necessary that all available dimensions are carrying equal information in the classification context. Selection of only a useful fraction of the available dimensions will result in huge data compression. In this talk we will present an algorithmic approach by means of which such an optimal subset can be selected.
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